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| Title | A New Approach For Iot-Based Fall Detection System Using Commodity Mmwave Sensors |
|---|---|
| ID_Doc | 2972 |
| Authors | Wang K.; Zhan G.; Chen W. |
| Year | 2019 |
| Published | ACM International Conference Proceeding Series |
| DOI | http://dx.doi.org/10.1145/3377170.3377211 |
| Abstract | Falling poses a significant risk to the elderly people living alone, and timely detection when a fall occurs to an elderly has attracted much research attention. Among the variety of technologies proposed, mmWave sensor based technology has shown promising performance in this task due to its strength in body-pose recognition. However, previous studies have focused on using sophisticated mmWave radars, whereas cheaper commodity mmWave sensors have not received much attention due to their low spatial resolutions. In this paper, we propose a fall detection system based on commodity mmWave sensors along with body-feature estimation algorithm to overcome the low-resolution deficiency. We first compare several body-feature estimation algorithms and select the optimized one. Then we illustrate the potential of body-feature estimation algorithms via a case study of a threshold-based fall detection system. We construct a database that includes 348 datasets; and our experimental evaluation of the datasets demonstrates that our algorithm is effective for detecting falls. © 2019 Association for Computing Machinery. |
| Author Keywords | Fall detection; IoT; MmWave sensor; Threshold-based classification; Wireless |
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